Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction.

BACKGROUND: Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation. METHODS: A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-systemic flow ratio (Qp:Qs) were evaluated by three experts and three trainees in pediatric cardiology and radiology using nine prespecified criteria. A separate set of 1,088 CXRs was used to train the DLM. The clinician-interpreted CXRs were subsequently assessed by the DLM to predict the Qp:Qs. RESULTS: Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (P < 0.001), with a root mean squared error of 0.395 (P < 0.001). The DLM showed a strong agreement with Fick-derived classification of pulmonary vascularity (κ = 0.770, P < 0.001; predicting Qp:Qs > 1.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs < 0.9: 92% specificity, 95% sensitivity, AUROC 98%). For interpreting pulmonary vascularity as increased or decreased, clinician concordance was 75% and 66%, respectively, whereas DLM concordance was 88% and 98%, respectively (P = 0.03). Among the structured criteria, the number of end-on vessels demonstrated strong interobserver agreement, whereas agreement for individual peripheral vessels, the size of end-on vessels, branch pulmonary arteries with their respective bronchi, and opacity of lung fields was modest. CONCLUSION: Deep learning-based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.